Dynamics of a learning controller for surface tracking robots on unknown surfaces

A Kalman-filter-based sensor fusion procedure is proposed for robotic manipulators on unknown curved surfaces. Models of ideal end-effector-surface contact properties are formulated in terms of a surface parameter vector. This vector becomes the state of an extended Kalman filter and completely defines the model of the surface. Filter input measurements can include, but are not limited to, force and joint kinematic data. It is assumed that control can be accomplished using existing techniques if accurate estimates of the surface normals (and hence, the tangent planes) are found. Therefore, a manipulator using incremental control based on local measurements might benefit from the online filter states. Filter covariance can then be considered an indicator for the point at which the unknown surface can be considered known.<
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